Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Mantel-Cox Log-Rank Test

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.

Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Truncation in Survival Analysis

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Unveiling the conversion mechanism of glioblastoma from the mesenchymal to the proneural subtype driven by HDAC1/p-SMAD3-TP53I11 axis.

Translational oncology·2026

Same author

Treatment of High-Risk Idiopathic Membranous Nephropathy with Huaier Granules and RASi: A Case Series.

International medical case reports journal·2026

Same author

Med-Diet: evaluation of an LLM-based system for clinically guided nutrition care in chronic diseases.

Frontiers in nutrition·2026

Same author

A pathological morphology parameter-based prognostic nomogram for high-risk prostate cancer patients treated with neoadjuvant therapy followed by radical prostatectomy: a retrospective study.

World journal of surgical oncology·2026

Same author

[Comparison of Imaging Efficacy and Patient Tolerability Between a Novel Cellulose-Based anda Conventional Starch-Based Oral Contrast Agent: A Prospective Randomized Controlled Trial].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae·2026

Same author

Author Correction: Hospital information system based psychological nursing improves maternal and neonatal outcomes in cesarean section patients.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

PENALIZED VARIABLE SELECTION PROCEDURE FOR COX MODELS WITH SEMIPARAMETRIC RELATIVE RISK.

Pang Du¹, Shuangge Ma, Hua Liang

¹Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA, pangdu@vt.edu.

Annals of Statistics

|August 31, 2010

Summary

This study introduces a penalized partial likelihood method for Cox models, effectively estimating parameters and selecting variables in both parametric and nonparametric components. The approach ensures accurate estimation and optimal convergence rates for complex survival data analysis.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jun 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Statistics
Biostatistics
Survival Analysis

Background:

Cox models are widely used for survival data analysis.
Semiparametric models offer flexibility by incorporating both parametric and nonparametric components.
Challenges exist in simultaneously estimating parameters and selecting variables in these complex models.

Purpose of the Study:

To develop a unified penalized partial likelihood procedure for Cox models with semiparametric relative risk.
To simultaneously estimate parameters and perform variable selection for both parametric and nonparametric parts.
To provide a robust method for analyzing complex survival data with potential nonlinear covariate effects.

Main Methods:

A penalized partial likelihood approach is proposed.
Sequential application of two penalties: a smoothing spline ANOVA framework for the nonparametric part and SCAD or adaptive LASSO for the parametric part.
Empirical model selection tools are derived for the nonparametric component.

Main Results:

The proposed estimator for the parametric part demonstrates the oracle property.
The estimator for the nonparametric part achieves the optimal rate of convergence.
Simulation experiments confirm the effectiveness of the proposed procedures.

Conclusions:

The developed method provides a powerful tool for semiparametric Cox model analysis.
The approach effectively handles variable selection and parameter estimation in both model components.
The method is applicable to real-world data, such as studies on sexually transmitted diseases.